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DB-Subdue: Database Approach to Graph Mining

  • Sharma Chakravarthy
  • Ramji Beera
  • Ramanathan Balachandran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3056)

Abstract

In contrast to mining over transactional data, graph mining is done over structured data represented in the form of a graph. Data having structural relationships lends itself to graph mining. Subdue is one of the early main memory graph mining algorithms that detects the best substructure that compresses a graph using the minimum description length principle. Database approach to graph mining presented in this paper overcomes the problems – performance and scalability – inherent to main memory algorithms. The focus of this paper is the development of graph mining algorithms (specifically Subdue) using SQL and stored procedures in a Relational database environment. We have not only shown how the Subdue class of algorithms can be translated to SQL-based algorithms, but also demonstrated that scalability can be achieved without sacrificing performance.

Keywords

Main Memory Extension Attribute Association Rule Mining Graph Mining Frequent Subgraph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Sharma Chakravarthy
    • 1
  • Ramji Beera
    • 1
  • Ramanathan Balachandran
    • 1
  1. 1.Information Technology Laboratory and CSE DepartmentThe University of Texas at Arlington 

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